Oral micronized progesterone for vasomotor symptoms—a placebo-controlled randomized trial in healthy postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare oral micronized progesterone (progesterone) with placebo as therapy for postmenopausal hot flushes and night sweats (vasomotor symptoms [VMS]). METHODS: Healthy volunteer community women 1 to 10 years since final menstruation were recruited for a randomized double-blind placebo-controlled trial of progesterone (300 mg daily at bedtime) between 2003 and 2009 and were screened for clinical, physical, or laboratory evidence of cardiovascular risks (nonsmoking, moderate body mass index [<35 kg/m], normal lipids, electrocardiogram, nondiabetic). Women recorded daily frequency and severity (1-4) of VMS in the Daily Menopause Diary during run-in (4 wk) and intervention (12 wk). Average daily VMS score (day frequency × day severity + night frequency × night severity) during final 28 therapy days was the primary outcome, analyzed by therapy, with run-in score as covariate. RESULTS: Randomized participants were 133 healthy community women with VMS, ages 44 to 62 years, with a mean (SD) VMS score of 17.0 (10.4) at run-in (VMS frequency 6.8 [3.2] episodes/d). Women were randomized to progesterone (n = 75) or placebo (n = 58); analysis included all with VMS data at run-in and on therapy (n = 68 and 46, respectively). The VMS scores of women taking progesterone were better than placebo (mean adjusted difference, -4.3 (95% CI, -6.6 to -1.9), with mean reductions of 10.0 (95% CI, -12.0 to -8.1) and 4.4 (95% CI, -6.6 to -2.2) in the progesterone and placebo arms, respectively. Discontinuation with adverse events was 9% (progesterone, 8; placebo, 4), with no serious cases. CONCLUSIONS: Oral micronized progesterone is effective for treatment of hot flushes and night sweats in healthy women early in postmenopause.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".